Derived relative motion information, which uses a dead reckoning process, is subject to cumulative error. Thus a tracking system relying on dead reckoning alone may have a continuous decrease in accuracy, which makes derived relative motion information not trustworthy over long periods of time. Many other aiding sensors have been considered, including ranging and optical based mapping systems.
The user track and map information that is acquired by use of multiple sensors, is combined so that the map information can compensate for dead reckoning, e.g., inertial drift while user motion/track information can allow perceptually aliased feature information to be disambiguated. Detected map features and ranges can feed into simultaneous localization and mapping (SLAM) algorithms which provide corrections based on the information. The SLAM algorithms are typically implemented using a version of Bayes filter such as a Kalman Filter.